Querying Two Virtual Organization Committees
Zhili Zhao1, Adrian Paschke1, Chaudhry Usman Ali2, and Harold Boley2,3
1 Computer Science Department, Freie Universit¨at Berlin, Germany {zhili.zhao,paschke}[AT]inf.fu-berlin.de,
2 Faculty of Computer Science, University of New Brunswick, Canada maniali[AT]gmail.com,
3 Institute for Information Technology, National Research Council Canada harold.boley[AT]nrc.gc.ca
Abstract. SymposiumPlanner-2011 is the newest in a series of Rule Responder instantiations for the Questions&Answers (Q&A) sections of the official websites of the RuleML Symposia. It supports committee members via personal agents based on member-encoded rule knowledge.
The personal agents are invoked by an organizational agent which se- lects them using a responsibility assignment matrix. This paper describes SymposiumPlanner-2011, which goes beyond the previous instantiations by providing a more powerful user interface and reducing redundancy in the internal conference data repositories.
1 Introduction
SymposiumPlanner4 is a series of Rule Responder[1, 5] instantiations for the Questions&Answers (Q&A) sections of the official websites of the RuleML Sym- posia since 2007. SymposiumPlanner utilizes an Organizational Agent (OA) to handle the filtering and delegation of incoming queries. Each committee chair has a Personal Agent (PA) that acts in a rule-based manner on behalf of the chair.
This PA manages the chair’s personal information, such as a FOAF(Friend of a Friend5)-like profile containing a layer of facts about the committee member as well as FOAF-extending rules. These facts and rules allow PAs to automatically respond to requests concerning the RuleML Symposia. Query responsibility for the organization is managed through a responsibility assignment matrix, which defines the classes of queries PAs of chairs are responsible for. External Agents (EAs) constitute the public interface to the OA of a Symposium’s virtual orga- nization, through which enquiry users can send queries and receive answers.
While the instantiations from 2007 to 2009 employed the same Rule Respon- der infrastructure, the 2010 implementation of SymposiumPlanner additionally incorporated the EMERALD6framework, which was built on top of the JADE7
4 http://ruleml.org/SymposiumPlanner/
5 http://www.foaf-project.org/
6 http://lpis.csd.auth.gr/systems/emerald/
7 http://jade.tilab.com/
multi-agent system. This project successfully bridged EMERALD and Rule Re- sponder, and added new functionalities such as the mapping of paper topics to Symposium tracks.
For the two installments of the 5th International RuleML Symposium8, ”RuleML 2011 - Europe” and ”RuleML 2011 - America”, our SymposiumPlanner-2011 employs two Sub-Organizational Agents (Sub-OAs) and an overarching Super- Organizational Agent (Super-OA). Along with the personal agents, these agents handle other issues associated with Super-OA-to-Sub-OA delegation, intelligent decision-tree-like Q&A, e.g. about where to submit theory papers, etc. We up- graded the communication middleware and rule agents of SymposiumPlanner to their latest versions and complement the user client to issue queries via a controlled natural language.
The rest of the paper is structured as follows: Section 2 introduces the SymposiumPlanner-2011 system and highlights its novel features. Section 3 de- scribes the implementation of SymposiumPlanner-2011 in detail. Based on our experience and analysis, we close with some suggestions about SymposiumPlan- ner’s future development.
2 SymposiumPlanner-2011 Use Case Description
The SymposiumPlanner-2011 agents support the organizing committee members in their organization tasks, such as: helping the program chair to monitor and possibly move important dates, finding contact information about selected chairs of the symposium, answering questions of participants about the conference, etc.
The organization of the symposium committee is modeled by a coordinator, who is responsible for communicating with the executors which complete tasks on behalf of the symposium chairs. In SymposiumPlanner, the coordinator is im- plemented as an organizational agent (OA), which knows the responsibilities and the roles of each executor (Symposium chair). The OA manages the executors, which are implemented as personal agents (PAs).
The 5th International Symposium on Rules 2011 has two installments ”RuleML 2011 - Europe” (IJCAI 2011) and ”RuleML 2011 - America” (BRF 2011). To better manage disparate symposiums, SymposiumPlanner-2011 provides three Organizational Agents (OAs) to model the overall RuleML-2011 organization and its two sub-committees of the two Symposium installments:
- The Super-OA delivers and filters queries and requested tasks to the Sub- OAs, single point of entry for that specific instalment of RuleML 2011.
- The Sub-OAs manage the organization committee members of the two RuleML- 2011 installments. They filter, decide and delegate incoming queries to re- sponsible personal agents in the appropriate sub-organization.
An ontological RAM represents the roles and responsibilities of the Sub-OAs and the personal agents in the virtual organization of RuleML Symposium 2011.
8 http://2011.ruleml.org
Table 1 gives a RAM fragment of the committee chairs of RuleML Symposium 2011. Negotiation and distributed coordination protocols are applied to manage and communicate with the organizations’ agents and external agents.
Table 1.Responsibility Assignment Matrix: Each committee chair has di↵erent roles, such as: responsible, supportive, consulted, informed, for a particular task.
General Chair Program Chair Publicity Chair ...
Symposium responsible consulted supportive ...
Website accountable responsible ...
Sponsoring informed, signs verifies responsible ...
Submission informed responsible ...
... ... ... ...
The personal agents are self-autonomous agents and have their own rule- based decision and behavioral logic on top of their personal information sources, Web services, vocabularies/ontologies and knowledge structures. The rules are executed by di↵erent platform-specific rule engines which communicate via an Enterprise Service Bus (ESB) using standardized Reaction RuleML messages (event messages).
Besides consulting knowledge from the internal data repositories, which was very common in previous instantiations, the SymposiumPlanner-2011 rule agents access external data sources to reduce redundancy in the conference data via query languages such as SQL, SPARQL, etc. External data sources such as cal- endars, vocabulary definitions, databases, web pages, metadata sources, personal data are dynamically queried at runtime and used as facts in the internal knowl- edge base of an agent. For instance, in SymposiumPlanner-2011, the rule agents access data coming from the Semantic Web Dog Food RDF database, which con- tains information about the RuleML-2011 proceedings such as authors, papers, reviewers.
As a Web-based Q&A application, the previous instantiations of Symposium- Planner allowed users to issue queries via selection of the queries in adaptable Web form menus. The potential queries needed to be defined before delivering the system to users. It becomes a real burden when there are many kinds of queries available. For the purpose of resolving this problem, SymposiumPlanner 2011 provides a translator service, which can automatically translate public in- terface descriptions of function rules to Reaction RuleML messages. Meanwhile, a controlled English has a restricted syntax and a restricted semantics described by a small set of construction and interpretation rules. It is not difficult for users to learn it. SymposiumPlanner 2011 also strives for allowing users to issue the queries in controlled English and then translate them into the standardized Reaction RuleML messages.
3 SymposiumPlanner-2011
SymposiumPlanner-2011 provides three Organizational Agents (OAs) to model the overall RuleML-2011 organization and its two sub-committees of the two Symposium installments. The general architecture of SymposiumPlanner 2011 is shown in Figure 1. In SymposiumPlanner-2011, we use the latest Prova 3.1.3 rule engine for the OAs, which is now an OSGi bundle and can run in an OSGi container with just a few dependencies. It packs a lot of features, some of them are inspired by the latest developments in modern computational logic, func- tional programming, distributed systems, and event driven architectures, some of them completely new and original. Some of very unique features include: reaction groups for event processing, guards and guarded cut, dynamic branches in work- flows, and etc. Meanwhile, we utilize latest Mule ESB 3.1, which includes major improvements to Cloud Connect, including custom schemas for each connector, much simpler invocation of connectors, a new polling mechanism, message en- richment capabilities, and a simple yet powerful logging facility. In what follows, we will detail the implementation of the SymposiumPlanner-2011 system.
Fig. 1. SymposiumPlanner 2011 Architecture: Each rule engine is implemented as a Web-based service consisting of a set of internal or external knowledge sources. Re- action RuleML (interchange language) messages are transported via the ESB to the appropriate agent with di↵erent transport protocols.
3.1 Enterprise Service Bus
Mule open-source ESB9allows deploying the rule-based agents on the Mule ob- ject broker and supports the communication in this rule-based agent processing network via a multitude of transport protocols. Mule provides a distributable object broker which follows the Staged Event Driven Architecture (SEDA)10pat- tern to manage all sorts of service components. This design decouples event and thread scheduling from application logic and avoids the high overhead associated with thread-based concurrency models[1].
The distributed agent services which at their core run the rule engines are in- stalled as Mule components which listen at configured endpoints, e.g., JMS mes- sage endpoints, HTTP ports, SOAP server/client addresses or JDBC database interfaces. Reaction RuleML11 is used as a common platform independent rule interchange format between the agents (and possibly other rule execution / in- ference services). The translator services are used to translate inbound and out- bound messages from platform-independent Reaction RuleML and/or controlled natural language into the platform-specific rule engines execution syntaxes and vice versa.
3.2 Prova Rule Engine
Generally speaking, each agent service might run one or more arbitrary rule engines. Here we only describe the rule engine Prova12 which is used for imple- menting the complex logic of the organizational agents. Prova follows the spirit and design of the recent W3C Semantic Web initiative and combines declarative rules, ontologies and inference with dynamic object-oriented Java API calls and access to external data sources via query languages such as SQL, SPARQL and XQuery [6].
File Input / Output ..., fopen(File,Reader), ...
XML (DOM)
document(DomTree,DocumentReader) :- XML(DocumenReader),...
SQL
... ,sql_select(DB,cla,[pdb_id,"1alx"],[px,Domain]).
RDF
...,rdf(http://...,"rdfs",Subject,"rdf_type","gene1_Gene"),...
XQuery
..., XQuery = ’for $name in StatisticsURL//Author[0]/@name/text() return $name’, xquery_select(XQuery,name(ExpertName)),...
SPARQL
...,sparql_select(SparqlQuery,...
Prova has its separation of logic, data access, and computation and its tight integration of Java and Semantic Web technologies. Due to the natural integra- tion of Prova with Java, it o↵ers an economic and compact way of specifying
9 http://www.mulesoft.org
10http://www.eecs.harvard.edu/⇠mdw/proj/seda/
11http://reaction.ruleml.org
12http://prova.ws
agents’ behavior while allowing for efficient Java-based extensions to improve performance of critical operations. The main language constructs of messaging reaction rules in Prova are: sendMsg, rcvMsg and rcvMult. For instance, the following query retrieves the tracks of the symposium RuleML 2011@IJCAI.
getTracks(XID,Track):-
% look-up responsible agent (Program Chair) from RAM
assigned(XID,Agent,ruleml2011ATijcai_ProgramChair,ruleml2011ATijcai_responsible),
% send the query to personal agent
sendMsg(XID,esb,Agent, "query", getTrack(Track)),
% receive answers multiple times
rcvMult(XID,esb,Agent, "answer", substitutions(Track)).
3.3 Reaction RuleML Rule Interchange Format
Reaction RuleML acts as an interchange language between distributed rule agents in SymposiumPlanner. It is a general, practical, compact and user-friendly XML-serialized sub-language of RuleML for the family of reaction rules and in- corporates various kinds of production, action, reaction, and knowledge repre- sentation temporal/event/action logic rules as well as (complex) event/action messages into the native RuleML syntax using a system of step-wise extensions [4]. For the communication between distributed rule-based (agent) systems Re- action RuleML provides a general message syntax as follows:
<Message directive="<!-- pragmatic context -->">
<oid> <!-- conversation ID--> </oid>
<protocol> <!-- transport protocol --> </protocol>
<sender> <!-- sender agent/service --> </sender>
<receiver> <!-- receiver agent/service --> </receiver>
<content> <!-- message payload --> </content>
</Message>
Distributed rule agents can be programmed by the proprietary languages and engines. Reaction RuleML provides a translator service framework which translates the rule messages from and to Reaction RuleML, controlled natural language and platform-specific rule languages, such as Prova, Drools, POSL, EMERALD, etc. For example, the query in Attempto Controlled English (ACE)
”What is the contact-information of the general-chair-of-RuleML-2011-IJCAI?”
can be translated into a Reaction RuleML query message, in order to get the contact information of the RuleML 2011@IJCAI general chair.
<RuleML>
<Message mode="outbound" directive="query-sync">
<oid><Ind>SymposiumPlannerSystem</Ind></oid>
<protocol><Ind>esb</Ind></protocol>
<sender><Ind>User</Ind></sender>
<content>
<Atom>
<Rel>getContact</Rel>
<Ind>ruleml2011ATijcai_GeneralChair</Ind>
<Var>Contact</Var>
</Atom>
</content>
</Message>
</RuleML>
This Reaction RuleML message is then translated into a Prova message query which is executed in the engine:
[SymposiumPlannerSystem,esb,User,query-sync,[getContact,ruleml2011ATijcai_GeneralChair,Contact]].
3.4 SymposiumPlanner User Client
The SymposiumPlanner user client supports two solutions to construct queries.
The straightforward solution uses an XML based file, which describes publicly accessibly rule functions together with their mode and type declarations, to create HTML forms. After user initializes the parameters, the translator service combines the structure of function rules and the parameters values to create the standardized Reaction RuleML message.
SymposiumPlanner user client also allows issuing queries in a controlled natural language (Attempto Controlled English [2]) via a web browser. The ACE2RML translator forwards the text to the Attempto Parsing Engine (APE), which translates the text into a discourse representation structure (DRS) and/or advices to correct malformed input. The DRS gives a logical/structural represen- tation of the text which will be fed to an XML parser and to be translated into a domain specific Reaction RuleML representation of the query. For example, the query follows ACE format: ”Who are the authors of ’Rule-based Distributed and Agent Systems’ ”? is translated by the APE into the following DRS:
<DRS domain="">
<Question>
<DRS domain="A B C">
<query obj="A" question="who" sentid="1" tokid="1"/>
<relation obj1="C" rel="of" obj2="string(’Rule-based Distributed and Agent Systems’)"
sentid="1" tokid="5"/>
<object ref="C" noun="author" struct="countable" unit="na" numrel="geq" num="2"
sentid="1" tokid="4"/>
<predicate ref="B" verb="be" subj="A" obj="C" sentid="1" tokid="2"/>
</DRS>
</Question>
</DRS>
By parsing the DRS and applying the domain-specific rules which map named objects to constants and predicate relations to atomic predicates. The core ele- ment of the result is shown as follows:
<Atom>
<Rel>getAuthorsOfPaper</Rel>
<Ind>’Rule-based Distributed and Agent Systems’</Ind>
<Var>author</Var>
</Atom>
4 Conclusion and Future Work
The communication middleware Mule uses SEDA, which decomposes the pro- cesses of Q&A in SymposiumPlanner with event-driven stages connected by ex- plicit queues. Theses queues enable Mule to decouple the receiver of a message from the other steps in processing the message. That means the operations of SymposiumPlanner lie mostly on the SEDA processing mechanism and its per- formance. As a well developed framework, the performance of SEDA has been evaluated by many e↵orts. In [7, 3], the authors shown that SEDA maximizes throughput and exhibits higher performance and more robust behavior under load than traditional service designs. Accordingly, these e↵orts can reflect the
performance of SymposiumPlanner from the other side, that is, Symposium- Planner can process users’ queries reasonably and prevent resources from being overcommitted when demand exceeds agent capacity.
In future, while there is always a potential need to add more human support to the system in order to enhance flexibility, we will also aim at achieving more efficiency in terms of responses from agents as they automate redundant tasks that human users can avoid and be able to respond to increasingly complex queries. This may also lead to the need for peer to peer communication between PAs in order to help each other in answering queries posed by external agents.
In such a scenario we can see PAs using FOAF-like profiles to advertise their capability of solving any query and cooperating with other PAs and the OA to achieve a collaborative environment of query response. In the case of a complex query, the query can be decomposed into parts, the OA can then delegate parts of the decomposed query to relevant PAs to solve individually. The OA can then eventually assemble the responses from PAs into a complete solution and send it back to the External Agent. In view of the aforementioned and proposed de- velopments in our e↵ort to build a framework to assist human organizations, we can see the SymposiumPlanner truly provides the basis for our gradual trans- formation of workplaces into an efficient and productive environment.
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